How Much VRAM to Run Llama 3 70B?
Llama 3.1 70B requires ~140GB VRAM at FP16 — you need multiple GPUs or heavy quantization. Q4_K_M brings it to ~44GB, making a single A100 80GB SXM viable for inference at good quality.
VRAM requirements by quantization level
| Quantization | VRAM needed | Quality | Fits on |
|---|---|---|---|
| FP16 | 140GB | Full quality | 4× A100 40GB · 2× A100 80GB · 2× H100 80GB |
| Q8_0 | 75GB | Near-lossless | A100 80GB SXM · 2× RTX 4090 · H100 PCIe |
| Q5_K_M | 55GB | Good | A100 80GB · L40S 48GB (pair) |
| Q4_K_M | 44GB | Good | A100 80GB · L40S 48GB · RTX 4090 (pair) |
| Q3_K_M | 34GB | Acceptable | A100 40GB · RTX 3090 (pair) · L40S |
GPU recommendations & rental prices
Verified GPU rental prices across all major providers. Start in minutes, pay by the hour.
Frequently Asked Questions
Can I run Llama 3 70B on a single GPU?
At Q4_K_M, yes — but only on A100 80GB or L40S 48GB. The RTX 4090 (24GB) cannot fit 70B even at Q4 on a single card. Two RTX 4090s in NVLink or tensor-parallel mode can fit Q4.
How does self-hosting 70B compare to API pricing?
A single A100 80GB on RunPod ($2.49/hr = $1,817/month) gives you approximately 600-900M tokens/day capacity at Q4 inference. Via Together AI at $0.23/M, that same token volume costs $138-207/day. Self-hosting breaks even at high sustained utilization (60%+ GPU usage around the clock).
Data verified 2026-08-20. GPU rental prices change frequently — verify before purchasing. See methodology.